Our client is seeking an experienced Staff Machine Learning Engineer. In this role, you will work on the development and optimization of machine learning models, with a particular focus on Knowledge Graphs, Graph Databases, and the integration of Large Language Models (LLMs). You will contribute to building next-generation systems that empower our data-driven products, collaborating with cross-functional teams to deliver impactful solutions.
Key Responsibilities:
- Lead the design, implementation, and optimization of machine learning models using graph-based approaches, with a focus on Knowledge Graphs and Graph Databases.
- Develop and apply advanced techniques in Natural Language Processing (NLP) and Large Language Models (LLMs) to enhance data-driven applications and decision-making processes.
- Collaborate with data engineers and software developers to integrate machine learning models into production systems, ensuring scalability and efficiency.
- Utilize graph theory and algorithms to solve complex problems in data representation, classification, recommendation, and reasoning.
- Conduct research and stay up-to-date on emerging technologies in machine learning, knowledge graphs, and language models to continuously improve the team's capabilities.
- Mentor junior engineers and provide technical leadership, fostering a collaborative and innovative engineering culture.
Required Qualifications:
- Master's or Ph.D. in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
- 5+ years of professional experience in machine learning, data science, or a related field, with a focus on graph technologies and large-scale systems.
- Strong expertise in Knowledge Graphs, Graph Databases (e.g., Neo4j, Amazon Neptune, etc.), and related algorithms.
- Hands-on experience with Large Language Models (LLMs) such as GPT, BERT, or similar, and applying them to real-world problems.
- Proficiency in programming languages such as Python, Java, or Scala, and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong understanding of distributed computing and data processing systems (e.g., Spark, Hadoop, Kubernetes).
- Excellent problem-solving, analytical, and debugging skills.
- Strong communication skills and the ability to work collaboratively in a fast-paced, team-oriented environment.
Preferred Qualifications:
- Experience with deploying machine learning models into production environments, including cloud platforms such as AWS, Azure, or GCP.
- Familiarity with additional graph-based technologies, such as GraphQL or SPARQL.
- Research experience in NLP, graph theory, or other advanced machine learning techniques.
